AI 中文总结
针对6G中NTN信道估计问题，提出基于轻量级卷积的信道估计器，在严格功率预算下评估其精度及对链路性能的影响，与现有算法相比，该方法在MSE方面有改善，还提高了参数效率并减少浮点运算。
AI 中文摘要
人工智能（AI）方法在地面网络中展现出强大的信道估计性能，但通常依赖大量计算资源。随着6G迈向统一架构，从一开始就将包括非地面网络（NTN），不能想当然地认为有大量且耗电的计算资源可用。同时，NTN传播常呈现高可预测性、有限多径丰富度和显著多普勒频移，这是个特定的信道估计问题。本文提出一种专为NTN运行和实时机载推理设计的基于轻量级卷积的信道估计器。在严格的非地球静止轨道（NGSO）功率预算下评估其信道估计精度，并量化对链路性能的端到端影响。结果表明，与现有算法相比，该方法在均方误差（MSE）方面有改善，证明高效AI模型即使在功率受限的星载节点上也能提供稳健性能。此外，通过利用领域知识，所提设计与最先进AI模型相比，参数效率提高27%，浮点运算比传统方法少约29倍，同时实现了卓越的MSE性能。
英文摘要
Artificial Intelligence(AI) methods have shown strong channel estimation performance in terrestrial networks, but they typically rely on substantial computational resources. As 6G moves toward a unified architecture that will include Non-Terrestrial Networks (NTN) from day 0, availability of large and power hungry computational resources shall not be taken for granted. At the same time, NTN propagation often exhibits high predictability, limited multipath richness and significant Doppler shifts, representing a specific channel estimation problem. In this work, we propose a lightweight convolution-based channel estimator designed specifically for NTN operation and real-time onboard inference. We evaluate its channel estimation accuracy under stringent NGSO power budgets and quantify the resulting end-to-end impact on link performance. We show the improvement in terms of Mean Squared Error (MSE) achieved by the proposed approach compared with established algorithms, demonstrating that efficient AI models can deliver robust performance even on power-constrained spaceborne nodes. In addition, the proposed design by exploiting the domain knowledge, improves parameter efficiency by $27\%$ compared with state-of-the-art AI models and requires approximately $29\times$ fewer floating-point operations than conventional methods while achieving superior MSE performance.
CommentsIEEE SPAWC 2026
